Yuan Ma

Reliable and Efficient Sub-Nyquist Wideband Spectrum Sensing in Cooperative Cognitive Radio Networks

The rising popularity of wireless services resulting in spectrum shortage has motivated dynamic spectrum sharing to facilitate efficient usage of the underutilized spectrum. Wideband spectrum sensing is a critical functionality to enable dynamic spectrum access by enhancing the opportunities of exploring spectral holes, but entails a major implementation challenge in compact commodity radios that only have limited energy and computation capabilities. In contrast to the traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, this paper proposes a sub-Nyquist wideband spectrum sensing scheme that locates occupied channels blindly by recovering the signal support, based on the jointly sparse nature of multiband signals. Exploiting the common signal support shared among multiple secondary users (SUs), an efficient cooperative spectrum sensing scheme is developed, in which the energy consumption on wideband signal acquisition, processing, and transmission is reduced with detection performance guarantee. Based on subspace decomposition, the low-dimensional measurement matrix, computed at each SU from local sub-Nyquist samples, is deployed to reduce the transmission and computation overhead while improving noise robustness. The theoretical analysis of the proposed sub-Nyquist wideband sensing algorithm is derived and verified by numerical analysis and further tested on real-world TV white space signals. It shows that the proposed scheme can achieve good detection performance as well as reduce the computation and implementation complexity, in comparison with the conventional cooperative wideband spectrum sensing schemes.

Sub-Nyquist rate wideband spectrum sensing over TV white space for M2M communications

Secondary operation in TV White Space (TVWS) calls for fast and accurate spectrum sensing over a wide bandwidth, which challenges the traditional spectrum sensing methods operating at or above Nyquist rate. Sub-Nyquist sampling has attracted significant interests for wideband spectrum sensing, while existing algorithms can only work for sparse spectrum with high computation and hardware complexity. In this paper, we propose a novel sub-Nyquist wideband sensing algorithm that can work for the non-sparse spectrum without sampling at full bandwidth through the use of multiple low-speed Analog-to-Digital Converters (ADCs) based on sparse Fast Fourier Transform (sFFT). The proposed permutation and filtering algorithm achieves the wideband sensing regardless of signal sparsity with low hardware complexity. In contrast to existing sub-Nyquist approaches, the proposed wideband sensing algorithm subsamples the wideband signal, and then directly estimates its frequency spectrum. The mathematical model of the proposed sub-Nyquist wideband sensing algorithm is derived and verified by numerical analysis over TVWS signals. The proposed algorithm shows considerable detection performance on wideband signals as well as reduces the runtime and implementation complexity in comparison with conventional wideband sensing algorithm.

Adaptively Regularized Compressive Spectrum Sensing from Real-Time Signals to Real-Time Processing

Wideband spectrum sensing is regarded as one of the key features in cognitive radio systems. Compressive sensing (CS) has recently become one of the promising techniques to deal with the Nyquist sampling rate bottleneck of wideband spectrum sensing. Theoretical analyses and simulation have shown that CS could achieve high detection probability and low false alarm for wideband spectrum sensing. However, implementation of CS on the real-time signals and real-time processing poses significant challenges due to the iterative nature of the CS algorithms. In this paper, we propose a novel adaptively regularized iterative reweighted least squares (AR-IRLS) algorithm to implement the real-time signal recovery on the CS based wideband spectrum sensing. The proposed algorithm moves estimated solutions along an exponential-linear path by regularizing weights with a series of non- increasing penalty terms, which significantly speeds up the convergence of reconstruction and provides high fidelity guarantee to cope with the varying bandwidths and power levels of occupied channels. The proposed algorithm presents robustness against different sparsity levels at low compressive ratio without degradation on the reconstruction performance, and is tested on the real-time signals over TV white space spectrum after having been validated on the simulated signals. Both the simulation and real-time experiments show that the proposed algorithm outperforms the conventional iterative reweighted least squares (IRLS) algorithms in terms of convergence speed, reconstruction accuracy, and compressive ratio requirement.

Efficient Blind Cooperative Wideband Spectrum Sensing Based on Joint Sparsity

Wideband spectrum sensing is a critical functionality in cognitive radio networks to enable dynamic spectrum sharing, but entails a major implementation challenge in compact commodity radios with restricted energy and computation capabilities. Exploiting jointly sparse nature of multiband signals, this paper proposes an efficient blind sub-Nyquist cooperative wideband spectrum sensing scheme, which reduces energy consumption in wideband signal acquisition, processing and transmission, with performance guarantee. In contrast to traditional sub-Nyquist approaches where a wideband signal or its power spectrum is first reconstructed from compressed samples, the proposed scheme locates occupied channels by recovering the signal support jointly from multiple secondary user (SU) measurements. Based on subspace decomposition, the low-dimensional measurement matrix computed at each SU from local sub-Nyquist samples can reduce transmission overhead while improving noise robustness. Numerical analysis and simulation results show that the proposed scheme can achieve good detection performance as well as reduce computation and implementation complexity in comparison with conventional cooperative wideband spectrum sensing schemes.

Optimization of collaborating secondary users in a cooperative sensing under noise uncertainty

Cooperative spectrum sensing is employed in Cognitive Radio (CR) networks to reliably detect Primary User (PU) transmissions by fusing the sensed data of multiple Secondary Users (SUs). The local detection reliability of an individual SU is closely related to its channel condition. In this paper, we propose a scheme that uses SNR to evaluate the reliability of each individual SUs local decision. We optimize the number of SUs for the sensing based on their channel conditions to achieve the optimal global detection probability at the fusion centre. Simulation results show that the proposed algorithm is robust against noise uncertainty with the optimal number of SUs and better receiver operating characteristic (ROC) performance is obtained in comparison to conventional schemes.

Sparsity Independent Sub-Nyquist Rate Wideband Spectrum Sensing on Real-Time TV White Space

Wideband spectrum sensing is a highly desirable feature in cognitive radio systems when the aim is to increase the probability of exploring spectral opportunities. Sub-Nyquist sampling has attracted significant interest for wideband spectrum sensing, while existing algorithms can only work with a sparse spectrum. In this paper, we propose a sub-Nyquist wideband spectrum sensing algorithm that achieves wideband sensing independent of signal sparsity without sampling at full bandwidth by using the low-speed analog-to-digital converters (ADCs) based on sparse fast Fourier transform. To lower signal spectrum sparsity while maintaining the channel state information, we preprocess the received signal through a proposed permutation and filtering algorithm. The proposed wideband spectrum sensing algorithm subsamples the time-domain signal and then directly estimates its frequency spectrum. We derive and verify the proposed algorithm by numerical analysis and test it on real-world TV white space signals. The results show that the proposed algorithm achieves high detection performance on sparse and nonsparse wideband signals with reduced runtime and implementation complexity in comparison with the conventional wideband spectrum sensing algorithms.

Autonomous compressive spectrum sensing approach for 3.5 GHz shared spectrum

The underutilized 3.5 GHz shared spectrum poses an excellent opportunity and potential for more intensive secondary usage by innovative applications/services. To find more spectral holes, a wide portion of spectrum must be sensed, which requires high sampling rates and a lot of measurements to be processed. Compressive sensing (CS) has recently become one of the promising techniques to deal with the sampling rate bottleneck of the wideband spectrum sensing. However, there are two significant challenges in the implementation of CS based wideband spectrum sensing: 1) no apriori knowledge of users activity statistics and 2) the varying bandwidth of channels and power levels. To address these issues, we proposed an autonomous compressive spectrum sensing approach that enables a secondary user to choose the number of measurements automatically, while the exact wideband signal reconstruction is guaranteed without assumption on spectral sparsity or channel characteristics. Specifically, the compressive measurements are collected block-by-block while the spectral is gradually reconstructed and the measurements collection process can be terminated once the variation of the Euclidean distance among the sequence of recovery solutions falls below a desired tolerance.

Joint Sub-Nyquist Spectrum Sensing Scheme With Geolocation Database Over TV White Space

To maximize spectrum access opportunities for white space devices, incorporating real-time spectrum sensing with geolocation database is a promising approach to enhance detection resolution with reduced computation complexity. Advanced spectrum sensing techniques are needed to quickly and accurately identify spectrum occupancy over a wide frequency range. However, the stringent requirements from wideband signal acquisition and processing pose a major implementation challenge in compact devices with limited energy storage and computation capabilities. In this paper, a hybrid scheme of sub-Nyquist wideband spectrum sensing with geolocation database is proposed to achieve accurate detection of the surrounding spectrum with reduced number of required measurements and computation complexity. Two iterative algorithms are modified to incorporate a priori information from geolocation database, therefore enabling spectrum sensing to be performed only on a limited number of potentially vacant channels over TV white space. Theoretical analyses and simulation results show that the proposed joint scheme speeds up the sensing process with enhanced detection performance and smaller required sampling rate, whereas the updated channel information from wideband spectrum sensing reduces the risk of interferences to the dynamic incumbent users.

RealSense: Real-time compressive spectrum sensing testbed over TV white space

Nowadays, wideband spectrum sensing, as one of the vital technologies of cognitive radio (CR), has the potential to find more temporarily available frequency bands to meet the growing demands of wireless services. As the vast number of samples are required to be collected and processed, traditional wideband spectrum sensing methods become inefficient and cause large energy consumption. Therefore, many theoretical work focus on applying compressive sensing (CS) into wideband spectrum sensing to alleviate this issue. In this paper, to verify the CS-based spectrum sensing scheme in real-world scenarios, a real-time compressive spectrum sensing testbed is proposed to process the real-time data collected from the TV white space (TVWS) spectrum. The proposed testbed consists of two parts: a senor node, and a real-time signal processing platform based on National Instruments (NI) LabVIEW software to process the spectral data and control the sensor.

Real-time Adaptively-Regularized Compressive Sensing in Cognitive Radio Networks

Wideband spectrum sensing is regarded as one of the key functional blocks in cognitive radio systems, where compressive sensing (CS) has become one of the promising techniques to deal with the Nyquist sampling rate bottleneck. Theoretical analyses and simulations have shown that CS could achieve both high detection and low false alarm probabilities in wideband spectrum sensing. However, the implementation of CS over real-world signals and real-time processing poses significant challenges due to the high computational burden and reconstruction errors against noise. In this paper, we propose an efficient adaptively regularized iterative reweighted least squares (AR-IRLS) algorithm to implement the real-time signal recovery in CS-based wideband spectrum sensing. The proposed AR-IRLS algorithm moves the estimated solutions along an exponential–linear path by regularizing weights with a series of nonincreasing penalty terms, which significantly speeds up the convergence of reconstruction and provides a high fidelity guarantee to cope with spectral signals with varying bandwidths and power levels. Furthermore, a descent-based decision threshold setting algorithm is proposed to distinguish the primary signals from the mixture of the reconstruction errors and unknown noises. The proposed scheme demonstrates robustness against different sparsity levels at low compressive ratios without degradation of the reconstruction performance. It is tested with the real-world signals over the TV white space after being validated with the simulated signals. Both the simulation and real-time experiments show that the proposed scheme outperforms the conventional iterative reweighted least squares algorithms in terms of convergence speed, reconstruction accuracy, and compressive ratio.